Since May 2020 Mirai Solutions is showing a dashboard on our gallery that contains a global view of the COVID-19 Pandemic, with a further split by continent and country. We use publicly available data from the COVID-19 Data Hub, a great open source project providing a unified data set put together from numerous official local sources from all over the world.
In October and December 2021 we published 2 articles “A closer look at Vaccination breakthroughs in Switzerland” and “A 2nd look at Vaccination breakthroughs in Switzerland”, where we showed how to read data from the Swiss Federal Office for Public Health (BAG) in R, and illustrated the difference in Hospitalizations and Deaths between Vaccinated and Unvaccinated during the weeks in October and December.
Here we provide a live update to these articles embedded in shinyapps.io that will always show the latest data from BAG.
To see how we read BAG data in R please refer to the previous article.
We are interested in the weekly BAG reports about vaccination breakthroughs occurred in the last 4 weeks for different age classes, see data documentation and our source: opendata.swiss.
The data documentation makes us aware of the following restrictions and warnings about the collected data:
To solve the former problem (1.) we will use the average of the vaccinated an unvaccinated population sizes across the month.
As of Today, (2022-02-04), the 4 last weeks considered are: 22-W-01, 22-W-02, 22-W-03, 22-W-04, i.e. in the interval from 2021-12-31 to 2022-01-28.
The age categories have been redefined again as: 0-19, 20-39, 40-59, 60-79, 80+.
The current situation in the last 4 weeks as of 2022-01-28: how the infections, hospitalizations and deaths occurred across the age classes in absolute terms is being shown below. Overall Switzerland has registered 853’836 infections, 3’171 hospitalizations and 390 deaths.
To account for different distribution of the population in the Age Classes consider the Cases per 100’000 inhabitants:
Infections happen more frequently in younger age classes (at least in absolute terms) while Hospitalizations and Deaths are more common among the older ones.
The current vaccination status per age group as of 2022-01-28, the “Fully Vaccinated” population is split according to the occurred injection of the Booster dose.
67.97% are fully vaccinated (2 doses), 69.67% have received at least one dose, while 35.32% of Swiss residents have received the booster dose.
This page mainly focuses on the comparison between the Vaccinated and Unvaccinated. It is worth first highlighting the differences between the 2 populations that would bias such comparison. The biggest is the younger age of the “Unvaccinated” population, less likely to be impacted by Covid-19. For this reason the data are grouped in Age Classes, even within the same class, age has a certain variability and there are other differences to consider that may make a population more or less inclined to infection, and hence to hospitalizations.
If we can assume that “Vaccinated” with 2 doses and those recovered with one dose have a similar protection, we can’t do the same for “Unvaccinated” and those recovered from Covid. Unfortunately we cannot extract relevant information from BAG that would allow us to exclude the already Infected from the Unvaccinated population. We can show here the % of total contagion in the global population and warn the readers that a “small” % of the “Unvaccinated” is NOT unprotected (leading to underestimation of the positive effect of vaccination).
| Table 0: Confirmed Infections per Age Class. 2022-01-28 | |||
| Population | Infections | Percentage | |
|---|---|---|---|
| 0-19 | 1’733’962 | 400’304 | 23.1 % |
| 20-39 | 2’290’857 | 667’494 | 29.1 % |
| 40-59 | 2’512’448 | 550’974 | 21.9 % |
| 60-79 | 1’712’190 | 197’840 | 11.6 % |
| 80+ | 459’898 | 64’302 | 14 % |
| All | 8’709’355 | 1’881’388 | 21.6 % |
It is worth mentioning other possible sources of bias that can’t be isolated, some of these differences could actually cause a bias in both directions.
We are happy to hear more from the readers about this topic and possibly collect sources that could give a better insight. We can neglect of course possible causes of bias for Infections (e.g. lower test tendency of the “Vaccinated”) that would not lead to a possible hospitalization, as Infections are not treated in this article.
A view of the absolute figures of all vaccination categories, including “Unknown”, i.e. not reported.
Overall the vaccination status is <“Unknown” for 19.9 % of the Hospitalized and for 19.7 % of the Deaths.
| Table 1: absolute entries per age and vaccination status. (2021-12-31,2022-01-28) | |||||||||||||||||
| Population | Hospitalizations | Deaths | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Unknown | Fully vac. Booster | Fully vac. No Booster | Partially vac. | Unvac. | Unknown | Fully vac. Booster | Fully vac. No Booster | Partially vac. | Unvac. | Unknown | Fully vac. Booster | Fully vac. No Booster | Partially vac. | Unvac. | |||
| 0-19 | 0 | 61’638 | 323’079 | 48’487 | 1’300’758 | 62 | 1 | 18 | 0 | 173 | 1 | 0 | 0 | 0 | 0 | ||
| 20-39 | 0 | 567’164 | 1’064’064 | 40’813 | 618’815 | 91 | 23 | 70 | 4 | 143 | 0 | 0 | 0 | 0 | 2 | ||
| 40-59 | 0 | 990’822 | 977’482 | 32’072 | 512’072 | 100 | 60 | 141 | 3 | 272 | 6 | 2 | 5 | 0 | 14 | ||
| 60-79 | 0 | 1’113’910 | 389’467 | 18’746 | 190’067 | 188 | 196 | 263 | 6 | 444 | 15 | 8 | 19 | 2 | 72 | ||
| 80+ | 0 | 342’950 | 89’161 | 7’939 | 19’847 | 189 | 203 | 194 | 5 | 322 | 55 | 33 | 38 | 3 | 115 | ||
| All | 0 | 3’076’485 | 2’843’254 | 148’058 | 2’641’559 | 630 | 483 | 686 | 18 | 1’354 | 77 | 43 | 62 | 5 | 203 | ||
There is no hint of whether the “Unknown” entries tend to be more or less vaccinated (checking their curves in the BAG site they seem to be somewhere in between), therefore it can make sense to reassign proportionally these cases to the others vaccination categories.
| Table 4: entries per age and vaccination status. Reallocation of Unknown vaccination status. (2021-12-31,2022-01-28) | ||||||||||||||
| Population | Hospitalizations | Deaths | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fully vac. Booster | Fully vac. No Booster | Partially vac. | Unvac. | Fully vac. Booster | Fully vac. No Booster | Partially vac. | Unvac. | Fully vac. Booster | Fully vac. No Booster | Partially vac. | Unvac. | |||
| 0-19 | 61’638 | 323’079 | 48’487 | 1’300’758 | 1 | 24 | 0 | 229 | 0 | 0 | 0 | 0 | ||
| 20-39 | 567’164 | 1’064’064 | 40’813 | 618’815 | 32 | 97 | 6 | 197 | 0 | 0 | 0 | 2 | ||
| 40-59 | 990’822 | 977’482 | 32’072 | 512’072 | 73 | 171 | 4 | 329 | 3 | 6 | 0 | 18 | ||
| 60-79 | 1’113’910 | 389’467 | 18’746 | 190’067 | 237 | 317 | 7 | 536 | 9 | 22 | 2 | 83 | ||
| 80+ | 342’950 | 89’161 | 7’939 | 19’847 | 256 | 245 | 6 | 406 | 43 | 49 | 4 | 148 | ||
| All | 3’076’485 | 2’843’254 | 148’058 | 2’641’559 | 598 | 853 | 23 | 1’697 | 54 | 77 | 6 | 251 | ||
After this reallocation let’s look at the records over 100’000 people in each reference age class and vaccination status, and at the ratio between the “Unvaccinated” and “Vaccinated” cases, in order to understand better the associated impact of vaccination. This view will be used also in the following sections. When accounting for the total, i.e. “All” Age Class, age adjusted figures have been computed accounting for the different distribution of the Vaccinated and Unvaccinated groups across the different Age Classes.
The ratio of the impact per 100’000 people of the “Fully Vaccinated” vs the “Unvaccinated” measures the vaccination effect and the risk of Hospitalization or Death of the “Unvaccinated” versus the “Vaccinated”.
| Table 5: entries over 100’000 people per age and vaccination status. Reallocation of Unknown vaccination status. (2021-12-31,2022-01-28) | |||||||||||
| Hospitalizations | Deaths | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Over 100k | Ratio over Unvac. | Over 100k | Ratio over Unvac. | ||||||||
| Fully vac. Booster | Fully vac. No Booster | Fully vac. Booster | Fully vac. No Booster | Fully vac. Booster | Fully vac. No Booster | Fully vac. Booster | Fully vac. No Booster | ||||
| 0-19 | 2.1 | 7.4 | 8.2 | 2.4 | 0 | 0 | |||||
| 20-39 | 5.6 | 9.1 | 5.7 | 3.5 | 0 | 0 | |||||
| 40-59 | 7.3 | 17.5 | 8.8 | 3.7 | 0.3 | 0.7 | 13.5 | 5.3 | |||
| 60-79 | 21.2 | 81.5 | 13.3 | 3.5 | 0.8 | 5.6 | 52.7 | 7.8 | |||
| 80+ | 74.6 | 274.4 | 27.4 | 7.5 | 12.4 | 55 | 60.2 | 13.6 | |||
| All | 12.1 | 39.4 | 16 | 4.9 | 0.9 | 4.2 | 55 | 11.7 | |||
The measures in the table above for the Age Class “All” are age adjusted. The estimate indicate that the “Unvaccinated” people have 16 times higher risk to be hospitalized, 55 times higher risk to die compared with a “Fully Vaccinated with Booster”, while 4.9 and 11.7 times higher compared with “Fully Vaccinated without Booster”.
Assuming there are 3 possible Scenarios to add to the current one: what if there had been no vaccinated at all this month? Or if we had been all vaccinated? What if all with Booster?
These opposite scenarios can be generated and compared with what really happened in the last 4 weeks (“Current”) by taking the Hospitalization and Death rates over 100’000 people of the unvaccinated (“No Vac.”) and vaccinated (“Vac. Booster” / “Vac No Booster”) populations and project them over the full population.
Worth mentioning that the protection given by the vaccines against infection is also to consider as source of bias in this scenario analysis:
Despite the decay of vaccination benefits over time and against infections (Omicron), this is still a factor to consider.
The cases per 100’000 people in the 3 scenarios + Current are presented below:
More importantly, projecting the values of the 3 scenarios on the whole population we can evaluate the vaccination impact in absolute terms.
| Table 6: Scenarios (a,b,c) per age and vaccination status. Reallocation of Unknown vaccination status. (2021-12-31,2022-01-28) | |||||||||
| Hospitalizations | Deaths | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| No Vac. | Current | Vac. No Booster | Vac. Booster | No Vac. | Current | Vac. No Booster | Vac. Booster | ||
| 0-19 | 305 | 254 | 128 | 37 | 0 | 1 | 0 | 0 | |
| 20-39 | 730 | 331 | 208 | 128 | 7 | 2 | 0 | 0 | |
| 40-59 | 1615 | 576 | 439 | 184 | 88 | 27 | 17 | 7 | |
| 60-79 | 4827 | 1097 | 1395 | 364 | 745 | 116 | 96 | 14 | |
| 80+ | 9409 | 913 | 1262 | 343 | 3440 | 244 | 253 | 57 | |
| All | 16886 | 3171 | 3431 | 1056 | 4281 | 390 | 366 | 78 | |
If there had been no vaccination at all, in the last 4 weeks there would have been 16’886 Hospitalizations and 4’281 Deaths, on the contrary, if all had received booster, there would have been 1’056 Hospitalizations and 78 Deaths. These figures are provided reconciling the totals summing the counts in the age classes.
This section reports how the cases developed over time within the 3 populations. Please note, differentiating according to the actual date of vaccination (e.g. if earlier than or within 6 months) is not possible.
Figures per 100k people are shown again, reallocating those in the “Unknown” category in each analyzed week.
The “Partially Vaccinated” population has been removed while the Booster status is shown only since 21-W-49, i.e. when Booster doses had been administered to at least 0.1% of the population. In order to include more data also during low-waves periods the age Classes are restricted to 4: 0-39, 40-69, 70+. We also reduce the time-line to start from week 21-W-27, corresponding to the date 2021-07-11.
In this part the calculations done so far are replicated for each week in the time-line, i.e. for a given week the report’s figures related to its past 4 weeks are being recalculated. In this way the lines can appear smoother and make the estimates more reliable (at least for over 40 where there are enough data).
The curves related to the whole population (“All”) look closer because the “Unvaccinated” population is much younger (they are not age adjusted).
Select the Hospitalized or Deaths cases and the Vaccination Status.